Learning hidden influences in large-scale dynamical social networks: A data-driven sparsity-based approach
arXiv:2007.06713
Abstract
Interpersonal influence estimation from empirical data is a central challenge in the study of social structures and dynamics. Opinion dynamics theory is a young interdisciplinary science that studies opinion formation in social networks and has a huge potential in applications, such as marketing, advertisement and recommendations. The term social influence refers to the behavioral change of individuals due to the interactions with others in a social system, e.g. organization, community, or society in general. The advent of the Internet has made a huge volume of data easily available that can be used to measure social influence over large populations. Here, we aim at qualitatively and quantitatively infer social influence from data using a systems and control viewpoint. First, we introduce some definitions and models of opinions dynamics and review some structural constraints of online social networks, based on the notion of sparsity. Then, we review the main approaches to infer the network's structure from a set of observed data. Finally, we present some algorithms that exploit the introduced models and structural constraints, focusing on the sample complexity and computational requirements.
References in corpus (20)
- Statistical mechanics of complex networks
- The structure and function of complex networks
- The Emerging Field of Signal Processing on Graphs: Extending High-Dimensional Data Analysis to Networks and Other Irregular Domains
- Statistical physics of social dynamics
- A Tutorial on Modeling and Analysis of Dynamic Social Networks. Part I
- Learning graphs from data: A signal representation perspective
- Novel Multidimensional Models of Opinion Dynamics in Social Networks
- Revealing Network Connectivity From Dynamics
- Signals on Graphs: Uncertainty Principle and Sampling
- Structural balance and opinion separation in trust-mistrust social networks
- Individualization as driving force of clustering phenomena in humans
- Signal Processing on Graphs: Causal Modeling of Unstructured Data
- Recent advances in opinion propagation dynamics: A 2020 Survey
- Causal Network Inference via Group Sparse Regularization
- Is there negative social influence? Disentangling effects of dissimilarity and disliking on opinion shifts
- Opinion control in complex networks
- Ego Network Structure in Online Social Networks and its Impact on Information Diffusion
- Active Sensing of Social Networks
- Getting started in probabilistic graphical models
- Gossips and Prejudices: Ergodic Randomized Dynamics in Social Networks